Credit Values Research Articles

Concordit: A credit-based incentive mechanism for permissioned redactable blockchain

Malicious attacks and the introduction of illegal data put blockchains at risk, and blockchain governance is gaining increasing attention. The redactable blockchain technology has become a mainstream solution for blockchain governance. However, a low completion rate for redaction tasks limits current redactable blockchain technologies, primarily due to the absence of an effective incentive mechanism for participants. This gap underscores the urgent need for designing and implementing robust incentive mechanisms in redactable blockchains. Incentive mechanisms can motivate and guide entities to participate and perform desired behaviors through awards and punishments. This paper proposes Concordit, the first deployable credit-based incentive mechanism for redactable blockchains. Its purpose is to encourage submitters to submit legal redaction requests, modifiers to perform legal redaction operations, and verifiers to maintain the behavior consistent with the consensus algorithm. In the context of permissioned blockchains, Concordit utilizes a credit value system for awards and punishments. Additionally, we use a game theory-based mechanism to analyze and model participants’ behavior utilities in the redactable blockchain. Meanwhile, we evaluate the credibility of nodes by combining their static initial credit values and dynamic behavior-related credit values. This system prioritizes high-credibility nodes as participants, thereby enhancing the completion rate for redaction tasks. Finally, the implementation and performance evaluation of our Concordit incentive mechanism demonstrate its effectiveness and practicality.

CE-PBFT: An Optimized PBFT Consensus Algorithm for Microgrid Power Trading

Currently, in the blockchain-based distributed microgrid trading system, there are some problems, such as low throughput, high delay, and a high communication overhead. To this end, an improved Practical Byzantine Fault Tolerance (PBFT) algorithm (CE-PBFT) suitable for microgrid power trading is proposed. First, a node credit value calculation model is introduced, and nodes are divided into consensus, supervisory, and propagation nodes according to their credit values, forming a hierarchical network structure to ensure the efficiency and reliability of consensus. Secondly, the consensus process is optimized by adopting a segmented consensus mechanism. This approach calculates the consensus rounds for nodes and selects the methods for node-type switching and consensus based on these calculations, reaching dynamic changes in node states and credit values, effectively reducing the communication overhead of node consensus. Finally, the experiments show that compared with the IMPBFT and PBFT algorithms, the CE-PBFT algorithm has better performance in throughput, delay, and communication overhead, with a 22% higher average throughput and 15% lower average delay than the IMPBFT algorithm and a 118% higher average throughput and 87% lower average delay than the PBFT algorithm.

CG-PBFT: an efficient PBFT algorithm based on credit grouping

Because of its excellent properties of fault tolerance, efficiency and availability, the practical Byzantine fault tolerance (PBFT) algorithm has become the mainstream consensus algorithm in blockchain. However, current PBFT algorithms have problems such as inadequate security of primary node selection, high communication overhead and network delay in the process of consensus. To address these problems, we design a novel efficient Byzantine fault tolerance algorithm based on credit grouping, called CG-PBFT. First, we propose a new credit evaluation model to obtain nodes’ credit values and introduce an optimized three-way quick sorting algorithm to divide nodes into the master-node group, the consensus-node group and the observation-node group, which have different privileges. The nodes in the observation-node group are restricted from participating in consensus, which reduces the communication overhead and improves consensus efficiency. Second, we propose an optimized selection method for the primary node based on a voting mechanism whereby the consensus-node group and observation-node group vote to produce the primary node, which reduces the probability of malicious nodes acting as the primary node and improves the security of primary node selection. Finally, the identity conversion mechanism between node groups is designed, and the actual behavior of nodes within different groups is given credit rewards or punishment, so as to keep an incentive for nodes to participate in appropriate system behavior and improve the working enthusiasm of nodes. The experimental simulation results show that compared with existing PBFT algorithms, the CG-PBFT algorithm improves the average throughput by 51.3% and reduces the average delay by 64.5%; it greatly improves the operating efficiency of the system and can be more suitable for application in the consortium blockchain scenarios.

Production flexibility and trade credit under revenue uncertainty

AbstractIn an uncertain economic environment, the ability of firms to adapt their production levels to unforeseen market demand is critical for their investment, financing and trade credit policies. This paper focuses on the impact of production flexibility on trade credit values and maturity in the presence of uncertain demand. We develop a continuous‐time real options framework where a buyer firm with capacity constraints orders input goods on credit from a supplier. The supplier optimally chooses the trade credit maturity, considering its influence on the buyer's optimal quantities and default timing. We distinguish between flexible firms, capable of suspending production during adverse conditions, and rigid firms, constrained to constant full‐scale production. Our findings reveal that production flexibility positively affects trade credit values and maturity. Flexible firms invest in larger capacity, default later and order larger quantities, resulting in higher trade credit values. Suppliers extend longer maturities to flexible firms, reflecting their higher creditworthiness and the positive effects of extended trade credit on their installed capacity. Furthermore, we explore extensions to our framework, including switching costs, entry timing, interactions between debt and trade credit and a non‐cooperative bargaining game.

Cooperative multi-agent game based on reinforcement learning

Multi-agent reinforcement learning holds tremendous potential for revolutionizing intelligent systems across diverse domains. However, it is also concomitant with a set of formidable challenges, which include the effective allocation of credit values to each agent, real-time collaboration among heterogeneous agents, and an appropriate reward function to guide agent behavior. To handle these issues, we propose an innovative solution named the Graph Attention Counterfactual Multiagent Actor–Critic algorithm (GACMAC). This algorithm encompasses several key components: First, it employs a multi-agent actor–critic framework along with counterfactual baselines to assess the individual actions of each agent. Second, it integrates a graph attention network to enhance real-time collaboration among agents, enabling heterogeneous agents to effectively share information during handling tasks. Third, it incorporates prior human knowledge through a potential-based reward shaping method, thereby elevating the convergence speed and stability of the algorithm. We tested our algorithm on the StarCraft Multi-Agent Challenge (SMAC) platform, which is a recognized platform for testing multi-agent algorithms, and our algorithm achieved a win rate of over 95% on the platform, comparable to the current state-of-the-art multi-agent controllers.

Using industrial wastes for rice-wheat cropping and food-energy-carbon-water-economic nexus to the sustainable food system

This experiment aimed to develop a long-term sustainable food-energy-water-carbon-economic nexus module by converting industrial wastes (IWs) to compost. In the methodology, a split-plot design was employed to apply a different recommended dose of fertilizer (RDF) levels, and carbon decomposing microbes (CDMs) converted IWs compost in the rice-wheat cropping. Four RDF levels were used in the main-plot, and nine combinations of CDMs + IWs were used in the sub-plot and replicated thrice. The result indicated that in the interaction impact of nitrogen, phosphorus, potassium (NPK)100% +zinc (Zn)5kg + iron (Fe)5kg × IW1+CDM1 produced 80.2% more grain yield, 68.1 and 176.2% more net energy output and energy balance. There was higher water productivity and lower WFP than those for NPK0 × IW3+CDM3. Further, the main-plot NPK100% +Zn5kg + Fe5kg was observed sequentially at a maximum of US$ 126 and 2149/ha carbon societal and credit value. Meanwhile, the IW1+CDM1 noted a higher US$ 124 and 2094/ha of carbon societal and credit values, respectively in the sub-plot. The first two components from principal component analysis (PCA) of various energy indices and carbon dynamics explained 81 and 14% of the variability, respectively. The hypothesis of the experiment was that the use of IWs combined with inorganic fertilizers and CDMs for rice-wheat cropping; may help to increase crop productivity, carbon dioxide (CO2) sequestration, soil organic carbon (SOC) pools, and reduce carbon footprints (CFs), water footprint (WFP), energy, and fertilizer load to feed the ever-growing population.

Assessing and improving the outcomes of nonpoint source water quality trading policies in urban areas: A case study in Virginia

Nonpoint source (NPS) water quality trading (WQT) is a market-based approach to improving water quality. Past work has shown that these programs could increase localized pollutant loadings, in part by exporting water quality controls from urban to rural areas. Virginia's NPS WQT program has enabled thousands of transactions and may provide a model for other programs, but its impacts on urban water quality have not been thoroughly assessed. We quantify the impact of NPS WQT purchases in Virginia on water quality and hydrology in an urban catchment. We go on to assess outcomes of a policy alternative where buyers and sellers are collocated in the urban catchment. Simulation results show that NPS WQT increased total phosphorus (TP) loading by an average of 0.8 lbs TP/year for each 1.0 offsite credits purchased in the analyzed catchment. The TP loading increased in years with greater rainfall, such that TP loads were increased by up to 1.2 lbs TP/year for each offsite credit purchased. These loading increases may or may not be acceptable, depending on the cumulative number of purchases within an urban catchment and existing local water quality issues. In our policy alternative with buyers and sellers collocated in the catchment, we found that the TP increase from development was completely offset at the catchment scale, with a decrease of 4.3 lbs TP/year for each 1.0 credits purchased. This suggests that credits awarded for urban mitigation practices are undervalued compared with water quality requirements for credit purchasers. This undervaluation is a result of the Virginia trading program using one approach to compute the credit value for buyers and a different approach to compute the credit value for sellers. We demonstrate how using a single model to determine both buyer and seller credit values in urban areas could provide greater transparency and mitigate the risk of urban pollution hot spots. This work demonstrates the importance of consistency in the scale of pollutant load calculations between buyers and sellers for NPS WQT, and contributes novel insight into the implications of WQT for urban NPS pollution.

Cost Assessment of Centralizing Swine Manure and Corn Stover Co-Digestion Systems

Livestock in the state of Iowa, United States (US) produce over 50 × 106 Mg of wet-basis manure yearly. Biogas production from manure’s anaerobic digestion (AD) can reduce greenhouse gas emissions, control odors, and provide renewable energy. Despite these benefits, AD is rarely deployed at swine farms in Iowa. In this work, we explore the economics of AD systems in Iowa to evaluate reasons for low deployment and explore the production cost impacts of biogas cleaning and injection into the natural gas grid, amending manure with biomass, and centralizing digesters across multiple farms. This work presents a static, spreadsheet-based technoeconomic model that embodies literature-based estimates of key system technical parameters, costs, and transportation fuel incentives and permits the examination of various scenarios. Key findings include that under the model assumptions, distributed, farm-scale digesters are not competitive with average natural gas prices in Iowa. A centralized production scenario can be competitive, provided that programs such as the low-carbon fuel standard (LCFS) and the renewable fuel standard (RFS) have sufficiently high credit values.

International Green building systems and Egyptian Green Pyramid system: a comparative study

The construction sector causes bad environmental effects. Consequently, several worldwide Green Building Rating Systems (GBRSs) have been created delivering a standard Credit Criteria (CC) for evaluating Green Building functioning. On the other hand, there is an obvious absence of study on determining a baseline to build novel Green Building Rating System and to assess the current Systems. Therefore, this study determines main Credit Criteria using a general literature review, and aims to assess these Criteria based on International Green Building Systems and Egyptian Green Pyramid System. The analysis is built on a numerical quantity which is acquired throughout giving credit values of chosen systems to the recognized main Credit Criteria. The assessment of these Credit Criteria can be assumed as a reference to create novel Green Building Rating System and it supplies effective outcomes to improve the current system further. It is discovered that ‘Energy’ yardstick is the greatest commonly believed main criteria and then followed by ‘Water’ criteria. On the other hand, Credit Criteria such as ‘Financial aspects’, ‘Sustainable designing’, ‘Stakeholder relationships’ and ‘waste reduction’ are recognized as potential Credit Criteria which can be involved in upgrading the assessment system in the future.

Geographically-resolved evaluation of the economic and environmental services from renewable diesel derived from attached algae flow-ways across the United States

Harmful algal blooms (HABs) are becoming more invasive and ever more prevalent due to rises in nitrogen and phosphorus pollution in watersheds. Herein, we introduce a novel biorefinery that taps into the vast sources of nitrogen and phosphorus in watersheds. Contaminated water is diverted to flow over attached algae systems, feeding native, periphytic algal cultures and scrubbing excessive nutrients from the water with the produced biomass converted to fuels through hydrothermal liquefaction. The evaluation of the biorefinery concept is done with sustainability analysis that incorporates geographically-resolved growth modeling with nutrient resource availability based on all Hydrologic Unit Code-8 (HUC8) in the contiguous US. Life cycle analysis results demonstrate a global warming potential of 25 g CO2-eq MJ−1, a eutrophication potential of 1.3 ∗ 10−5 kg N eq. MJ−1, and a net energy ratio 0.33 of MJ MJ−1 in the Santa Monica Bay, CA subbasin. Techno-economic assessments found that renewable diesel can be produced for $1.20 per cubic decimeter (dm−3) or $4.56 per gallon of gasoline equivalent (GGE−1) under optimal conditions in the Santa Monica Bay, CA subbasin, with results dramatically varying across the US. Water quality trading was also incorporated into the analysis. Using modest nutrient credit values of $4.5 per kg of total nitrogen (kg-TN−1) and $4.5 per kg total phosphorus (kg-TP−1) removed enabled the renewable diesel to achieve parity with conventional diesel, $1.01 dm−3 ($3.84 GGE−1) in the Santa Monica Bay, CA subbasin. A more aggressive credit value of $45 kg-TN−1 and $45 kg-TP−1 made the price of the renewable diesel negative in Santa Monica Bay, CA, roughly $-4.45 dm−3 ($-16.8 GGE−1), and across the Midwest, the Gulf of Mexico, and major cities on the East and West Coast. These results highlight a path forward for mitigating eutrophication while also creating a sustainable fuel.

Review of Continuing Medical Education in Tick-Borne Disease for Front-Line Providers.

Considering increasing rates of tick-borne diseases (TBDs) in the United States, we investigated the scope of continuing medical education (CME) available to physicians on these infections. We surveyed online medical board and society databases serving front-line primary and emergency/urgent care providers for the availability of TBD-specific CME between March 2022 and June 2022. We recorded and analyzed opportunity title, author, web address, publication year, learning objectives, CME credit values, and CME credit type. We identified 70 opportunities across seven databases. Thirty-seven opportunities focused on Lyme disease; 17 covered nine non-Lyme TBDs, and 16 covered general topics on TBDs. Most activities were hosted through family medicine and internal medicine specialty databases. These findings suggest limited availability of continuing education for multiple life-threatening TBDs of increasing importance in the United States. Increasing the availability of CME materials covering the broad scope of TBDs in targeted specialty areas is essential for increased content exposure and a necessary step to ensure our clinical workforce is adequately prepared to address this growing public health threat.

ЗАХОДИ МОНЕТАРНОЇ ПОЛІТИКИ НБУ ЩОДО ФІНАНСОВОЇ СТАБІЛЬНОСТІ В УКРАЇНІ В УМОВАХ ВІЙНИ

The Ukrainian economy has faced a challenging situation due to the full-scale invasion by Russia, resulting in heightened panic and a negative impact on the country's macroeconomic stability. High inflation rates in Ukraine, caused by Russia's full-scale military aggression, have significantly affected the economic situation, including price increases for goods and services and the devaluation of the currency. To restore stability and reduce inflationary pressures, the National Bank has implemented effective measures. The article delves into the effectiveness of the National Bank of Ukraine's monetary policy measures, such as liquidity injections and credit support, in stabilizing the financial system and facilitating economic resilience amid the challenging circumstances of the full-scale invasion. The main instruments of monetary policy in wartime conditions are identified. The necessity of implementing an expansionary monetary and credit policy in times of war is substantiated. Statistical data on the dynamics of the NBU's key policy rate, integral indicators of deposit and credit values in hryvnia, consumer price indices, official exchange rates of the hryvnia against the euro and the US dollar, NBU interventions, operations of individuals in the cash market, and Ukraine's international reserves from February 2022 to March 2023 are provided. The actions of the NBU focusing on financial stability, protection of the hryvnia, increased attractiveness of deposits, and alleviation of pressure on the foreign exchange market are discussed. Monetary policy measures such as changes in the key policy rate and foreign exchange interventions are also examined. It is projected that the NBU will continue to pursue an expansionary monetary and credit policy to stimulate the economy and contain inflationary pressures. These comprehensive measures aim to bolster the resilience of the Ukrainian economy and pave the way for sustainable recovery once the war subsides. The NBU remains vigilant and ready to adapt its monetary policy toolkit as needed to navigate the evolving circumstances and safeguard financial stability in the face of ongoing hostilities.

More risk, more money: When are payments for water savings from limited irrigation profitable for farmers?

As farmers in semiarid climates seek new ways of adding value to their operations, those with irrigation water rights are increasingly receptive to payments, or credits, for water sharing. Yet, past research on the economic feasibility of limited irrigation strategies for consumptive use (CU) savings seldom considers production risk. Using stochastic dominance, we compare the effect of three limited irrigation practices—deficit irrigation, root zone drying, and early crop maturity—on the returns to corn production for sprinkler and subsurface technology. Field-level simulations show that the practices reduce returns and increase risk, but credits for CU savings could make them economically viable for farmers. Larger credits (more money) incentivize limited irrigation at greater levels (less yield and more risk), but fully compensating farmers for risk-bearing will be costly. With sprinkler technology, root zone drying becomes risk-efficient at lower credit values than deficit irrigation. Deficit irrigation along with root zone drying become risk-efficient at the lowest credit values for subsurface technology. Thus, risk aversion could explain why some farmers choose not to share water even when credits are large enough, on average, to compensate for differences in expected returns. Improved knowledge about the profitability and risk of limited irrigation practices can increase the joint sustainability of irrigated agriculture and other societal water uses.

An efficient method to estimate renewable energy capacity credit at increasing regional grid penetration levels

The wide scale deployment of variable renewable energy technologies (VREs) offers a pathway to decarbonize the electric grid. One challenge to reliably operating the grid is ensuring that sufficient generating capacity is available to meet demand at all hours. By determining an individual generator's contribution to resource adequacy based on its expected availability when power is needed, the capacity credit for these resources is estimated. The objective of this study is to quantify the contribution of VRE to resource adequacy as a function of VRE penetration, across several regions, technologies, and resources. A computational model was built using the effective load carrying capability (ELCC) method to calculate capacity credit values for regions spanning the contiguous United States. As the deployment of VRE increases, we show its marginal contribution to meeting peak load decreases, which in turn requires additional generating capacity to maintain reliability. In addition, a rapid approximation method is demonstrated to estimate solar and wind capacity credit, relying on the capacity factors during hours of peak net demand. We find that estimates with the lowest error relative to capacity credits calculated using the ELCC method occur using the average renewable resource capacity factors of the top net 10 demand hours, regardless of resource type. Using context-specific values for capacity credit can improve long-term decision making in generation capacity expansion, cultivating more economical long-term resource planning for deep decarbonization.

ASSESSING LEED CORE AND SHELL (LEED–C-AND-S), V3 AND V4, OF GOLD OFFICE-TYPE PROJECTS: THE DIFFERENCE BETWEEN FINLAND AND SPAIN

ABSTRACT This study aims to compare the strategies that Finland and Spain have taken in order to get the Gold license from the Leadership in Energy and Environmental Design (LEED) Core and Shell (LEED–C-and-S) rating system. LEED–C-and-S version 3 (v3) and version 4 (v4) were considered. The absolute effect size is used to evaluate the performance of the LEED–C-and-S points. To assess the difference between Finland and Spain, we use the natural logarithm of the odds ratio and Fisher’s exact 2 × 2 test with Lancaster’s mid-p-value when analyzing the dichotomous data, and Cliff’s δ and the exact Wilcoxon–Mann–Whitney tests when analyzing ordinal data. As a result, in order to achieve the Gold level in LEED–C-and-S office-type projects, Finland and Spain demonstrated similarities and differences in credit values in v3 and v4. In v4 (latest version), the similarly high credits were location and transportation (LT) and water efficiency (WE) and similarly low credits were material and resource (MR) and environmental quality (EQ); different credit values were in the energy and atmosphere (EA) category, in which Finland outperformed Spain, and the sustainable sites (SS) strategy category, in which Spain outperformed Finland. Thus, Finland used the LT-WE-EA strategy, whereas Spain used the LT-WE-SS strategy. Knowing these strategies can be helpful in better understanding green building development in these countries.

An Online Transfer Learning Framework With Extreme Learning Machine for Automated Credit Scoring

Automated Credit Scoring (ACS) is the process of predicting user credit based on historical data. It involves analysing and predicting the association between the data and particular credit values based on similar data. Recently, ACS has been handled as a machine learning problem, and numerous models were developed to address it. In this paper, we address ACS issues concerning credit scoring in a batch of machine learning problems, namely, feature irregularities due to empty features in many records, class imbalance due to non-uniform statistical distributions of the records between classes, and concept drift due to changing statistical characteristics concerning certain classes and features with time. Considering the limited credit scoring data volume, we propose to address the challenge using the Transfer Learning with Lag (TLL) algorithm based on embedded shallow neural networks that enable knowledge transfer when the number of active features changes. Knowledge transfer is based on lags having an adaptive length that is changed based on performance change feedback. Furthermore, the framework proposes classifier aggregation and the chunk balancing mechanism for handling class imbalance. An evaluation was conducted using the Lending club, German, Default, and PPDai datasets. The results show the superiority of the proposed algorithm over the benchmarks in terms of the majority of classification metrics concerning both time series and overall results. TLL offered improvements of 58.6% and 28.2% over FA-OSELM and OSELM using the Lending club dataset.

Battery Energy Storage Contribution to System Adequacy

The objective of this paper is to evaluate the contribution of energy storage systems to resource adequacy of power systems experiencing increased levels of renewables penetration. To this end, a coherent methodology for the assessment of system capacity adequacy and the calculation of energy storage capacity value is presented, utilizing the Monte Carlo technique. The main focus is on short-duration storage, mainly battery energy storage systems (BESS), whose capacity values are determined for different power and energy configurations. Alternative operating policies (OPs) are implemented, prioritizing system cost or reliability, to demonstrate the significant effect storage management may have on its contribution to system adequacy. A medium-sized island system is used as a study case, applying a mixed integer linear programming (MILP) generation scheduling model to simulate BESS and system operation under each OP, in order to determine capacity contribution and overall performance in terms of renewable energy sources (RES) penetration, system operating cost and BESS lifetime expectancy. This study reveals that BESS contribution to system adequacy can be significant (capacity credit values up to ~85%), with energy capacity proving to be the most significant parameter. Energy storage may at the same time enhance system reliability, reduce generation cost and support RES integration, provided that it is appropriately managed; a combined reliability-oriented and cost-driven management approach is shown to yield optimal results.

A MADM Location Privacy Protection Method Based on Blockchain

Location-based services make life easier, but they also involve privacy leakage issues. Many location privacy protection algorithms have been proposed to protect the privacy of users. However, these algorithms are usually based on theoretical data, and there are no actual user data to support studies of location privacy protection. To address this problem, we introduce a credit value, convert credit data from users into credit values using the multiple-attribute decision making (MADM) algorithm, store the credit values and transaction information from the anonymous zone construction process in conjunction with a blockchain, and propose a credit value reward and punishment mechanism that treats anonymous zone construction as a two-party game between the requestor and participant. In this game, a credit value reward and punishment mechanism is used to constrain undesirable behaviors. Through simulation experiments, it is verified that the method can be applied in practical scenarios, effectively constrain undesirable user behaviors, quickly construct anonymous zones, and reduce the probability of user location leakage issues.

Analisis Kredit Macet pada Koperasi Sekolah Menengah Kejuruan

Abstrak - Koperasi merupakan suatu lembaga atau organisasi dalam melakukan simpan pinjam berupa uang dan barang supaya bisa mensejahterakan anggota koperasi tersebut namun dari hasil wawancara dengan ketua koperasi SMKN 1 Kota Bima ternyata masih banyak sebagian dari anggota koperasi yang tidak mebayar pinjaman ataupun tagihan kredit secara kontinyu sesuai dengan perjanjian kredit hal ini akan berdampak pada kondisi keuangan koperasi untuk pemberian kredit berikutnya kepada anggotanya, sehingga dalam Penelitian ini bertujuan untuk mengetahui seberapa besar kredit macet pada koperasi SMKN 1 Kota Bima untuk periode 2014-2019. Adapun sampel dalam penelitian ini penulis menggunakan laporan keuangan 6 tahun terakhir yaitu dari periode 2014-2019. Data yang digunakan dalam penelitian ini adalah dengan menggunakan data laporan keuangan berupa data kredit macet dan total kredit yang diberikan,data yang digunakan adalah data sekunder dan metode yang digunakan yaitu uji t one sample (uji 1 pihak). Dimana koperasi SMKN 1 Kota Bima ini belum bisa dikatakan baik karena Non Performing Loan (NPL) pada tahun 2015 sebesar 2,72, 2016 sebesar 1,41 , 2017 sebesar 0,75 dan 2019 sebesar 0,59 karena NPL yang baik itu sesuai dengan krireria yaitu harus kurang dari 0,05 atau 5% dari kriteria yang diharapkan hal serupa juga terlihat dari perolehan nilai kredit macet pada tahun 2015 sampai dengan 2019 mengalami fluktiatif hal ini disebabkan karena banyaknya dari sebagain anggota koperasi yang tidak membayar angsuran kredit secara tepat waktu padahal dikontrak perjanjian kredit sudah tertera jelas bahwa anggota akan memebayar kredit sesuai dengan keputusan yang telah disepakati bersama.
 Kata Kunci :NPL (Non Performing Loan), Koperasi, Kredit

Could congressionally mandated incentives lead to deployment of large-scale CO2 capture, facilities for enhanced oil recovery CO2 markets and geologic CO2 storage?

In passing the Bipartisan Budget Act of 2018, Congress reformed and strengthened a section of the tax code, 45Q, which provides tax credits of up to $35/ton CO2 for the capture and utilization of CO2 in qualifying applications such as enhanced oil recovery (EOR) and up to $50/ton CO2 for CO2 that is captured and permanently stored in a geologic repository. Earlier versions of the tax credit with lower credit values generated limited interest. This change to the tax code could potentially alter U.S. energy systems. This paper examines the effect of the increased 45Q credits on CO2 capture, utilization and storage (CCUS) deployment in the United States and on petroleum and power production. A range of potential outcomes is explored using five modeling tools. The paper goes on to explore the potential impact of possible modifications of the current tax credit including extension of its availability in time, the period over which 45Q tax credits can be utilized for any given asset and increases in the value of the credit as well as interactions with technology availability and carbon taxation. The paper concludes that 45Q tax credits could stimulate additional CCUS beyond that which is already underway.